Stock Price Prediction
26 papers with code • 1 benchmarks • 2 datasets
Stock Price Prediction is the task of forecasting future stock prices based on historical data and various market indicators. It involves using statistical models and machine learning algorithms to analyze financial data and make predictions about the future performance of a stock. The goal of stock price prediction is to help investors make informed investment decisions by providing a forecast of future stock prices.
Astock
Latest papers with no code
Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey
Predicting stock prices presents a challenging research problem due to the inherent volatility and non-linear nature of the stock market.
Contrasting the efficiency of stock price prediction models using various types of LSTM models aided with sentiment analysis
Our research aims to find the best model that uses companies projections and sector performances and how the given company fares accordingly to correctly predict equity share prices for both short and long term goals.
Stock Price Prediction using Dynamic Neural Networks
This paper will refute the EMH and support Chaos theory.
The Battle of Information Representations: Comparing Sentiment and Semantic Features for Forecasting Market Trends
To start, we demonstrate the connection of tweet sentiments with the volatility of companies' stock prices.
Stock Price Prediction Using Temporal Graph Model with Value Chain Data
Stock price prediction is a crucial element in financial trading as it allows traders to make informed decisions about buying, selling, and holding stocks.
Model-Agnostic Meta-Learning for Natural Language Understanding Tasks in Finance
Our contribution includes: 1. we explore the performance of MAML method with multiple types of tasks: GLUE datasets, SNLI, Sci-Tail and Financial PhraseBank; 2. we study the performance of MAML method with multiple single-type tasks: a real scenario stock price prediction problem with twitter text data.
DSE Stock Price Prediction using Hidden Markov Model
An HMM is trained by analyzing the fractional change in the stock price as well as the intraday high and low values.
Leveraging Vision-Language Models for Granular Market Change Prediction
Predicting future direction of stock markets using the historical data has been a fundamental component in financial forecasting.
Hidden State Approximation in Recurrent Neural Networks Using Continuous Particle Filtering
Using historical data to predict future events has many applications in the real world, such as stock price prediction; the robot localization.
FinBERT-LSTM: Deep Learning based stock price prediction using News Sentiment Analysis
Economy is severely dependent on the stock market.
